We compare our approach to several state-of-the-art methods: the out-of-distribution detector method (OD) [1]}, a generative approach to zero-shot action recognition (GGM) [2]}, the evaluation of output embeddings (SJE) [3]}, the feature generating networks (WGAN) [4]}, the end-to-end training for realistic applications approach (E2E) [5]}, the inverse autoregressive flow (IAF) based generative model, bi-directional adversarial GAN(Bi-dir GAN) [6]} and prototype sampling graph neural network (PS-GNN) [7]}. We use a pre-trained model on Kinetics.
